【Python】实现最大最小距离算法

# 最大最小距离算法的Python实现

# 数据集形式data=[[],[],...,[]]

# 聚类结果形式result=[[[],[],...],[[],[],...],...]

# 其中[]为一个模式样本，[[],[],...]为一个聚类

import math

def start_cluster(data, t):

zs = [data[0]] # 聚类中心集，选取第一个模式样本作为第一个聚类中心Z1

# 第2步：寻找Z2,并计算阈值T

T = step2(data, t, zs)

# 第3,4,5步，寻找所有的聚类中心

get_clusters(data, zs, T)

# 按最近邻分类

result = classify(data, zs, T)

return result

# 分类

def classify(data, zs, T):

result = [[] for i in range(len(zs))]

for aData in data:

min_distance = T

index = 0

for i in range(len(zs)):

temp_distance = get_distance(aData, zs[i])

if temp_distance < min_distance:

min_distance = temp_distance

index = i

result[index].append(aData)

return result

# 寻找所有的聚类中心

def get_clusters(data, zs, T):

max_min_distance = 0

index = 0

for i in range(len(data)):

min_distance = []

for j in range(len(zs)):

distance = get_distance(data[i], zs[j])

min_distance.append(distance)

min_dis = min(dis for dis in min_distance)

if min_dis > max_min_distance:

max_min_distance = min_dis

index = i

if max_min_distance > T:

zs.append(data[index])

# 迭代

get_clusters(data, zs, T)

# 寻找Z2,并计算阈值T

def step2(data, t, zs):

distance = 0

index = 0

for i in range(len(data)):

temp_distance = get_distance(data[i], zs[0])

if temp_distance > distance:

distance = temp_distance

index = i

# 将Z2加入到聚类中心集中

zs.append(data[index])

# 计算阈值T

T = t * distance

return T

# 计算两个模式样本之间的欧式距离

def get_distance(data1, data2):

distance = 0

for i in range(len(data1)):

distance += pow((data1[i]-data2[i]), 2)

return math.sqrt(distance)

if __name__=='__main__':

data = [[0, 0], [3, 8], [1, 1], [2, 2], [5, 3], [4, 8], [6, 3], [5, 4], [6, 4], [7, 5]]

t = 0.5 #比例因子

result = start_cluster(data, t)

for i in range(len(result)):

print("----------第" + str(i+1) + "个聚类----------")

print(result[i])